Infrastructure, institutional quality and infrastructure financing gaps: the casee of selected afreican countries
| dc.contributor.author | Nyamkure, Blondel | |
| dc.date.accessioned | 2023-11-30T08:59:47Z | |
| dc.date.available | 2023-11-30T08:59:47Z | |
| dc.date.issued | 2022 | |
| dc.description | A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy to the Faculty of Commerce, Law and Management, University of the Witwatersrand, 2022 | |
| dc.description.abstract | The aim of this thesis is to examine three related issues around institutions, infrastructure and economic growth of Sub-Saharan Africa (SSA) economies. The first essay (Chapter Two) chronicles the evolution of the SSA region’s gaps in infrastructure needs and financing. We observed that the SSA region’s infrastructure investment and financing gaps are widening and ballooning over time across the four infrastructure types. We also found evidence of positive and significant correlation between infrastructure quality and institutional quality among a sub-sample of former British colonies in the SSA region, which turn to be weak and insignificant in case of other former colonies. This is also influenced by the influence of the legal origins on the infrastructure development in the SSA region. Also, of interest is the observation that the SSA region’s debt is approaching unsustainable level, in which 40% of this debt burden is as a result of leakages through bureaucrats’ rent-seeking and managerial inefficiency tendencies necessitated by porous, opaque and weak institutions. This serves as a possible reason why the infrastructure investment and financing gaps for SSA economies is continuously widening over time despite funds; more of their funding requirements are lying idle under mutual management funds’ custody, which the region is failing to tape into. Therefore, to close its infrastructure investment and financing gaps, relevant authorities of SSA countries must develop great strong institutions, implement radical structural reforms which crowd-in and promote private sector participation in infrastructure projects. Also, given the complementarity between infrastructure stock and human capital, there is need to massively invest in soft infrastructure such as education and health care. Due to the conspicuous antecedent literature dearth on the institution-infrastructure nexus, the main thrust of the second essay (Chapter Three) is to estimate the static and dynamic threshold level of institutional quality that will ensure stimulation of infrastructure development through efficient use of public debt (PD), government (GR) financial sector funds (FMD) and services as well as crowd in foreign direct investment (FDI) inflows in a panel of 46 Sub-Saharan African region between 2000 and 2017. For robustness of results, we employed a Hansen fixed effects threshold approach, Fully Modified Ordinary Least Squares (FMOLS) static models and Seo and Shin (2016) and Seo et al. (2019) recent theorized dynamic panel threshold regression approaches as informed by the New Institutional Economics theory. For our estimation approach, we adopted non-linear ii asymmetric static and dynamic modeling which gained prominence in recent econometric literature. The dynamic panel data threshold model was estimated using the Seo and Shin (2016) theoretical first differenced Generalized Method of Moments (FDGMM) estimation technique as operationalized by Seo et al. (2019). Though it varies with the aspect of economic and political institutional quality measure and whether entangled or disaggregated, overall, the results revealed that the effect of institutional quality on infrastructure development is nonlinear, with thresholds ranging between 45 – 90% in the static case and 56 – 82% in the dynamic case. This provides support for the use of a threshold regression model, with institutional quality serving as the threshold variable. Further probing was done using PD, FMD, GR and FDI as additional transition variables. In terms of the threshold level, the findings show that the index of institutional quality that will ensure the efficient use of infrastructure in stimulating growth is 0.61. The study also found that, on average, most countries in the region are operating below this threshold level, hence their huge infrastructural investment and financing gaps as well as underdevelopment observed in Chapter Two. The conclusion that is drawn from the analysis is that poor institutional quality is one of the factors hampering development of infrastructure of SSA regional countries. Coupled with a huge debt burden, low institutional quality also hinders efficient allocation of funds by financial institutions towards infrastructure development and reduction of FDI inflow to SSA regional economies. Legal origin has also been found to play a pivotal role in shaping the SSA region’s state of infrastructure development. The major strength of this study is that the methodology employed for the threshold analysis is exhaustive since it encompasses both static and dynamic panel data models developed for single and multiple threshold(s) value(s). Weak political institutions have been found to be a major drag to SSA’s infrastructure development. Thus, it is recommended that governments in the region need to formulate and implement policies targeted at improving the level of economic and political institutional quality in their countries, which can crowd-in both domestic (public and private) and foreign infrastructure investors. Essay three (Chapter four) examines the non-linear threshold effect of infrastructure, quantity, quality and access on economic growth using a rich and robust sample of 46 SSA countries. The study was provoked by the observation that despite its largest infrastructure investment and financing gaps, the SSA region also scored the least on all infrastructure iii aspects across the globe. Furthermore, economy growth of SSA economies stalled post the global financial crisis and inequality and poverty trends were on an upward trajectory. From antecedent literature, we identified the following drawbacks: (a) The literature did not determine the minimum amount of infrastructure quality or quantity which is needed to boost growth; (b). Lack of ascertainment on whether infrastructure quality and quantity has a U or inverted U-shaped effect on growth; (c). Though infrastructure access is directly and indirectly linked to welfare issues, inclusive growth and SDGs, it was conspicuously omitted and(d). at the literature did not explore whether infrastructure quantity, quality and access has complementarity or substitutability effect on growth. These issues form the basis and contribution of this study to the body of existing knowledge. Thus, this study strived to close these gaps by employing a static model (Hansen fixed effects) and dynamic model (System Generalized Methods of Moments (SGMM). We found that, firstly, the infrastructure quantity, quality and access have positive effect on growth of the SSA region, with quantity on the top followed by access and lastly quality. Secondly, the three infrastructure dimensions act as complements as opposed to substitutability since combining the three infrastructure aspects yield more growth benefits than their individual effect. Thirdly, infrastructure quantity and quality have a non-linear U-shaped effect on economic growth of SSA with optimum minima thresholds levels of 60% and 71%, respectively while that of infrastructure quality is linear. Best growth benefits will accrue to the SSA region when the quantity dimension of infrastructure is combining or interacting with both quality and accessibility, followed by quantity and quality and lastly quantity and access. Fourth, relative to the world average benchmark (excluding SSA), the growth benefit of 14.25% per annum accrues to the SSA region if it closes its overall infrastructure gap. In a more granular form, closing the quantity infrastructure gap would deliver higher growth benefit of 8.67% per year, followed by increasing infrastructure accessibility of 3.92% per annum and lastly catching up in terms of quality will raise growth by 1.66% per year. We observed that major contributors were electricity power, road network and improved drinking water. We recommend that SSA governments, development financial institutions (DFIs) and the private sector across the globe, like sovereign wealth funds, pension funds, and insurance companies devote financial resources towards infrastructure investment in the SSA region for it to reach | |
| dc.description.librarian | PC(2023) | |
| dc.faculty | Faculty of Commerce, Law and Management | |
| dc.identifier.uri | https://hdl.handle.net/10539/37233 | |
| dc.language.iso | en | |
| dc.phd.title | PhD | |
| dc.school | Economics and Finance | |
| dc.subject | Infrastructure | |
| dc.subject | Economic growth | |
| dc.subject | Institutions | |
| dc.title | Infrastructure, institutional quality and infrastructure financing gaps: the casee of selected afreican countries | |
| dc.type | Thesis |